Summary­­

While we believe factors like value, momentum, carry, and trend are applicable at the asset class level, care must be taken in designing tactical allocation portfolios.

We outline four considerations that we believe can have an outsized impact on tactical portfolio performance if ignored.

For readers looking to employ tactical strategies, understanding these considerations will help you better evaluate a manager’s process and how they seek to control unintended risks.

Recently, we were invited to speak at IMN’s Evidence-Based Investing Conference (West) on a panel about tactical allocation.

Not surprising to anyone who reads our research, our comments on the panel were largely centered around the application of systematic style premia (e.g. value, momentum, carry, and trend) to tactical allocation.

At the end of the panel, during the Q&A session, one audience member asked the question, “are there special considerations that have to be made when applying factors at the asset allocation level versus the individual security level?”

That is a question we probably could write a book about (hint:we are).

The answer is that we believe there a number of considerations that need to be made, some of which can have an outsized impact on portfolio performance if ignored. In this commentary, we want to highlight four that we believe investors should be aware of when evaluating tactical managers.

Establishing Consistent Measures Across Asset Classes

In traditional factor / style premia investing, stocks are ranked by a scoring system (e.g. valuation, past returns, et cetera). A portfolio is then built by going long the top ranked stocks (e.g. the top quintile) and short-selling the bottom ranked stocks. This approach captures the performance spread between the two baskets.

For long-only portfolios, a similar effect is achieved by overweighting the top ranked stocks and underweighting the low ranked stocks – which is, in effect, the same as “overlaying” the long/short portfolio on top of a long-only market portfolio.

When constructing factor portfolios with stocks, the scoring system works best when it can be applied across all stocks and when it provides a consistent measure to compare them to each other. For example, the traditional equity value factor is elegant because all stocks can be ranked on price-to-book and price-to-book has a consistent meaning across all stocks.[1]

The same cannot be said for asset classes: for example, bonds and commodities do not have a price-to-book metric. How then should we measure value? While we might introduce other measures (e.g. real yield for bonds), this arguably increases model risk, as we now need to validate the efficacy of several unique metrics.

Furthermore, we now need to consider how we’re going to compare metrics across asset classes. If the real yield for bonds is 2% and the price-to-book value for the S&P 500 is 3, which asset class is cheaper?

To address this problem, some sort of normalization needs to be performed (e.g. using a more consistent measure – like yield – or z-scoring), which further introduces modeling and assumption risk. Additionally, we must consider how factor scores may translate to return differentials differently across asset classes or sub-asset classes. Within fixed income, for example, the impact of similar yield and spread changes will differ across portfolios with varying durations.

Fewer “Bets” To Make

When it comes to stock picking, we tend to see two approaches to managing risk: qualitative analysis and diversification. Discretionary managers tend to prefer the former while quants tend to leverage the latter.

While we often think of a long/short factor portfolio as one basket versus another, there is no reason we cannot alternatively think of it as a large number of pairs trades, where we are long one stock and short another.

For example, consider a simple value factor that is long the cheapest 100 stocks in the S&P 500 (based upon some valuation metric) and short the 100 most expensive stocks. The return of this long/short will equivalent to the spread in performance between the two portfolios, but we can also think of it as 100 different long/short pairs: long the top ranked stock, short the bottom ranked; long the 2nd best stock, short the 2nd worst; etc.[2]

In this way, a traditional stock-based factor portfolio is diversifying across a large number of bets. Each bet, individually, has a positive expected return, but may have a large variance. By diversifying across a large number of such bets, we can reduce exposure to the idiosyncratic risks that might drive any individual pair’s performance.

When it comes to tactical asset allocation, there are typically fewer bets that can be made. While today’s strategies often have greater breadth than the stock-bond-cash strategies of yesteryear, even a 10-asset portfolio will only result in 5 long/short pairs. Without significantly dropping down the asset class hierarchy to include geographic and sector distinctions, creating internal diversification can be difficult.

Cross-Asset Dynamics

When we go long one basket of stocks and short another, we have two fairly reasonable expectations: (1) the correlation between the baskets will be fairly high, and (2) the volatility of the baskets will be similar (at the very least, in orders of magnitude).[3]

This has important implications for the risk of a factor trade. Consider that the variance of a dollar-neutral long/short portfolio will be:

With similar variances and a high degree of correlation, we would expect the variance of the factor portfolio to be close to zero. Furthermore, both legs of the trade will, more or less, contribute to risk equally.

The same cannot be said for asset classes. Consider a naïve relative value trade that goes long stocks and short bonds.

There are a few effects to consider:

The correlation between stocks and bonds can vary dramatically over time, leading to a shifting risk profile for the trade.

The variance of stocks will (likely) swamp the variance for bonds, and thus the risk of the portfolio will largely be driven by what stocks do and not by what the pair does.

The relative volatility level is a particularly important effect to note. Consider a tactical trade placed with the expectation of a steepening yield curve. The curve can steepen by either a decrease in the short-end or an increase in the long-end. To play this trade, we can go long 2-year bonds and short 10-year bonds.

The problem with this trade is that 10-year bonds are much more sensitive to rate changes than 2-year bonds. If the 2-year rate falls 1% and the 10-year rate falls 0.2%, the curve has technically “flattened,” but our trade will not be profitable. For our trade to work, both legs of the trade have to have an equal amount of interest rate sensitivity (duration). Therefore, we need to buy approximately five 2-year bonds for each 10-year bond we short.[4]

Without leverage, our ability to profit on the trade is severely diluted.

A similar effect occurs with cross-asset trades. Even for two asset classes that share similar risk levels, accounting for unintended risk-factor exposures can be important. Consider going long a high-yield bond index and short a 5-year U.S. Treasury index with the intention of capturing a declining credit spread. Without explicit duration matching of the indices, there can be residual interest rate exposure. This latent exposure can be particularly harmful if we consider that rates and spreads can exhibit significant negative correlations during negative economic shocks or flight-to-safety periods.

Factors and style premia applied within an asset class can, largely, ignore these effects as risk factors are generally shared and stable. The same is not true for asset classes, and blindly applying the same approaches without acknowledging this difference can lead to unexpected results.

Tactical Often Increases Internal Portfolio Concentration

While cross-asset dynamics can play an important role in the construction of tactical portfolios, they can also play an important role in the question of, “should we bother being tactical at all?”

This was the topic of a commentary: Rising Correlations and Tactical Asset Allocation[5].

In the commentary, we used a simple example of a static 50/50 stock/bond portfolio, and a tactical strategy that can flexibly allocate between stocks and bonds. Our question was simple: when is it better to hold the tactical strategy and when is it better to just hold the static portfolio?

In many ways, the answer is a function of available diversification. Note that the TAA strategy will always be more concentrated in one asset class than the static benchmark is. Or, conversely, the TAA strategy will always be less diversified.

When correlations are high between stocks and bonds, there is little diversification benefit foregone by the increased concentration in the TAA strategy. On the other hand, when diversification opportunities abound, the hurdle rate for TAA to add value above-and-beyond a well-diversified portfolio increases dramatically.[6]

Note that this is not necessarily true for factor investing at the stock level. Switching from a passive equity index to a long-only value portfolio can actually introduce beneficial diversification benefits, as the equity beta exposure remains, but an “active beta” (i.e. the value strategy) is added as well. We saw this effect in our recent commentary Factors & Financial Planning.[7]

As somewhat of a tangent, an interesting byproduct of the changing hurdle rate for TAA is to “time our timing,” i.e. dial the magnitude of tactical decisions within the portfolio based upon internal diversification available within the benchmark policy portfolio declines. In Improving on risk parity[8], researchers from J.P. Morgan used a similar concept for dynamically allocating between a risk parity and mean-variance optimization process. They found that when there was significant dispersion between asset class Sharpe ratios, the forecast risk required by MVO was worth bearing, while it was better to use risk parity when Sharpe ratios converged.

Diversification is an important hedge against forecast uncertainty. TAA explicitly foregoes diversification in the pursuit of return. The trade-off, however, is not always straightforward. The time-varying nature of asset class dynamics means that an identical trade could have dramatically different hurdle rates for success depending on when it is made.

There may be times that diversification is so abundant that doing nothing is the best course of action.

Conclusion

As a firm that specializes in systematic tactical allocation, we believe strongly that active approaches can lead to more efficient portfolio constructions, particularly when based upon established style premia such as value, momentum, carry, and trend.

That said, we also recognize that utilizing style premia in a multi-asset fashion can introduce complexities, that when unaddressed, can lead to unexpected (read: poor) performance. Caveat emptor: understanding how a manager addresses these problems is critical for establishing long-term expectations.

[1] For the sake of brevity, we’re not going to comment on the ongoing efficacy of price-to-book, whether price-to-book is really applicable across all sectors, and whether there are structural sector-based considerations that need to be made when measuring “cheapness” or “richness.”

[2] For convenience, we’re just assuming that stocks within each leg are equally-weighted.

[3] The one exception here would be the low-volatility factor, but firms like AQR apply leverage to the lower volatility leg for this exact reason.

[4] We’re playing a bit fast-and-loose here with duration assumptions.

[6] This ignores the reality that the tactical signals employed may themselves be more accurate when correlations are lower; e.g. relative momentum tends to favor bonds during market crises, which is also when we tend to see negative correlations emerge.

Corey is co-founder and Chief Investment Officer of Newfound Research, a quantitative asset manager offering a suite of separately managed accounts and mutual funds. At Newfound, Corey is responsible for portfolio management, investment research, strategy development, and communication of the firm's views to clients.

Prior to offering asset management services, Newfound licensed research from the quantitative investment models developed by Corey. At peak, this research helped steer the tactical allocation decisions for upwards of $10bn.

Corey is a frequent speaker on industry panels and contributes to ETF.com, ETF Trends, and Forbes.com’s Great Speculations blog. He was named a 2014 ETF All Star by ETF.com.

Corey holds a Master of Science in Computational Finance from Carnegie Mellon University and a Bachelor of Science in Computer Science, cum laude, from Cornell University.

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